wehub-resource-sync
323 lines
10 KiB
Python
323 lines
10 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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from functools import partial
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import hypothesis.strategies as st
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import numpy as np
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from auto_scan_test import CutlassAutoScanTest, PassAutoScanTest
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from program_config import OpConfig, ProgramConfig, TensorConfig
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os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
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class TestTransferElimPass0(PassAutoScanTest):
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r"""input0 input1
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transfer_layout transfer_layout
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transfer_layout_out0 transfer_layout_out1
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\ /
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elementwise_add
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elementwise_add_out
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"""
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def sample_predictor_configs(self, program_config):
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# for gpu
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config = self.create_inference_config(use_gpu=True)
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yield config, ["elementwise_add", "transfer_layout"], (1e-4, 1e-5)
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def is_program_valid(self, prog_config):
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return True
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def sample_program_config(self, draw):
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transfer_layout0 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input0"]},
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outputs={"Out": ["transfer_layout_out0"]},
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dst_layout=1,
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src_layout=2,
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)
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transfer_layout1 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input1"]},
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outputs={"Out": ["transfer_layout_out1"]},
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dst_layout=1,
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src_layout=2,
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)
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add_op = OpConfig(
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"elementwise_add",
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inputs={
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"X": ["transfer_layout_out0"],
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"Y": ["transfer_layout_out1"],
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},
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outputs={"Out": ["elementwise_add_out"]},
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axis=-1,
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)
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ops = [transfer_layout0, transfer_layout1, add_op]
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x_shape = draw(
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st.lists(
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st.integers(min_value=10, max_value=100), min_size=4, max_size=4
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)
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)
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"input0": TensorConfig(shape=x_shape),
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"input1": TensorConfig(shape=x_shape),
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},
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outputs=["elementwise_add_out"],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=30,
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passes=["transfer_layout_elim_pass"],
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)
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class TestTransferElimPass1(PassAutoScanTest):
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r"""input0 input1
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transfer_layout transfer_layout
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transfer_layout_out0 transfer_layout_out1
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\ /
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elementwise_add
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elementwise_add_out
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transfer_layout
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transfer_layout2
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"""
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def sample_predictor_configs(self, program_config):
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# for gpu
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config = self.create_inference_config(use_gpu=True)
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yield config, ["elementwise_add"], (1e-4, 1e-5)
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def is_program_valid(self, prog_config):
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return True
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def sample_program_config(self, draw):
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transfer_layout0 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input0"]},
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outputs={"Out": ["transfer_layout_out0"]},
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dst_layout=1,
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src_layout=2,
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)
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transfer_layout1 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input1"]},
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outputs={"Out": ["transfer_layout_out1"]},
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dst_layout=1,
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src_layout=2,
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)
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add_op = OpConfig(
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"elementwise_add",
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inputs={
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"X": ["transfer_layout_out0"],
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"Y": ["transfer_layout_out1"],
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},
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outputs={"Out": ["elementwise_add_out"]},
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axis=-1,
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)
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transfer_layout2 = OpConfig(
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"transfer_layout",
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inputs={"X": ["elementwise_add_out"]},
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outputs={"Out": ["transfer_layout_out2"]},
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dst_layout=2,
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src_layout=1,
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)
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ops = [transfer_layout0, transfer_layout1, add_op, transfer_layout2]
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x_shape = draw(
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st.lists(
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st.integers(min_value=10, max_value=100), min_size=4, max_size=4
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)
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)
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"input0": TensorConfig(shape=x_shape),
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"input1": TensorConfig(shape=x_shape),
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},
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outputs=["transfer_layout_out2"],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=30,
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passes=["transfer_layout_elim_pass"],
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)
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class TestTransferElimPass2(PassAutoScanTest):
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r"""input0 input1
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transfer_layout transfer_layout
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transfer_layout_out0 transfer_layout_out1
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\ /
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concat
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concat_out
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"""
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def sample_predictor_configs(self, program_config):
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# for gpu
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config = self.create_inference_config(use_gpu=True)
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yield config, ["concat", "transfer_layout"], (1e-4, 1e-5)
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def is_program_valid(self, prog_config):
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return True
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def sample_program_config(self, draw):
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# nhwc -> nchw
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transfer_layout0 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input0"]},
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outputs={"Out": ["transfer_layout_out0"]},
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dst_layout=1,
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src_layout=2,
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)
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transfer_layout1 = OpConfig(
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"transfer_layout",
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inputs={"X": ["input1"]},
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outputs={"Out": ["transfer_layout_out1"]},
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dst_layout=1,
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src_layout=2,
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)
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concat_op = OpConfig(
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"concat",
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inputs={"X": ["transfer_layout_out0", "transfer_layout_out1"]},
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outputs={"Out": ["concat_out"]},
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axis=1,
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)
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ops = [transfer_layout0, transfer_layout1, concat_op]
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x_shape = draw(
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st.lists(
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st.integers(min_value=10, max_value=100), min_size=4, max_size=4
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)
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)
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"input0": TensorConfig(shape=x_shape),
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"input1": TensorConfig(shape=x_shape),
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},
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outputs=["concat_out"],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=30,
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passes=["transfer_layout_elim_pass"],
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)
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class TestTransferElimPass3(CutlassAutoScanTest):
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def sample_program_configs(self, *args, **kwargs):
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def generate_input(input_shape):
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return (np.random.random(input_shape) - 0.5).astype(np.float32)
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# src_layout should be NCHW, because it is the model's input
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for dst_layout, src_layout in [[1, 2]]:
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for axis in [0, 1, 2, 3]:
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ops_config = [
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{
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"op_type": "transfer_layout",
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"op_inputs": {"X": ["input0"]},
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"op_outputs": {"Out": ["transfer_layout_out0"]},
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"op_attrs": {
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"dst_layout": dst_layout,
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"src_layout": src_layout,
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},
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},
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{
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"op_type": "transfer_layout",
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"op_inputs": {"X": ["input1"]},
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"op_outputs": {"Out": ["transfer_layout_out1"]},
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"op_attrs": {
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"dst_layout": dst_layout,
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"src_layout": src_layout,
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},
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# nchw -> nhwc
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},
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{
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"op_type": "concat",
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"op_inputs": {
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"X": [
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"transfer_layout_out0",
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"transfer_layout_out1",
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]
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},
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"op_outputs": {"Out": ["concat_out0"]},
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"op_attrs": {"axis": axis},
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},
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]
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ops = self.generate_op_config(ops_config)
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input_shape = [12, 13, 14, 15]
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program_config = ProgramConfig(
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ops=ops,
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weights={},
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inputs={
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"input0": TensorConfig(
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data_gen=partial(generate_input, input_shape)
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),
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"input1": TensorConfig(
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data_gen=partial(generate_input, input_shape)
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),
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},
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outputs=["concat_out0"],
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)
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yield program_config
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_gpu=True)
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config.enable_use_gpu(256, 0)
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yield config, (1e-2, 1e-2)
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def test(self, *args, **kwargs):
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self.run_test(quant=False, *args, **kwargs)
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if __name__ == "__main__":
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unittest.main()
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